Lag effect of ambient temperature on respiratory emergency department visits in Beijing: a time series and pooled analysis

Background Although the association between ambient temperature and mortality of respiratory diseases was numerously documented, the association between various ambient temperature levels and respiratory emergency department (ED) visits has not been well studied. A recent investigation of the association between respiratory ED visits and various levels of ambient temperature was conducted in Beijing, China. Methods Daily meteorological data, air pollution data, and respiratory ED visits data from 2017 to 2018 were collected in Beijing. The relationship between ambient temperature and respiratory ED visits was explored using a distributed lagged nonlinear model (DLNM). Then we performed subgroup analysis based on age and gender. Finally, meta-analysis was utilized to aggregate the total influence of ambient temperature on respiratory ED visits across China. Results The single-day lag risk for extreme cold peaked at a relative risk (RR) of 1.048 [95% confidence interval (CI): 1.009, 1.088] at a lag of 21 days, with a long lag effect. As for the single-day lag risk for extreme hot, a short lag effect was shown at a lag of 7 days with an RR of 1.076 (95% CI: 1.038, 1.114). The cumulative lagged effects of both hot and cold effects peaked at lag 0–21 days, with a cumulative risk of the onset of 3.690 (95% CI: 2.133, 6.382) and 1.641 (95% CI: 1.284, 2.098), respectively, with stronger impact on the hot. Additionally, the elderly were more sensitive to ambient temperature. The males were more susceptible to hot weather than the females. A longer cold temperature lag effect was found in females. Compared with the meta-analysis, a pooled effect of ambient temperature was consistent in general. In the subgroup analysis, a significant difference was found by gender. Conclusions Temperature level, age-specific, and gender-specific effects between ambient temperature and the number of ED visits provide information on early warning measures for the prevention and control of respiratory diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-024-18839-6.


Supplemental Materials Table of Contents
Table S1 Relative risk of respiratory ED visits at specific ambient temperature.8

Fig S6.
Relationship between temperature and risk of respiratory diseases development at different lag days in Beijing.9 Table S2 Cumulative association and relative risk with different lag days of specific temperatures on respiratory ED visits.
Table S3 The relative risk of respiratory diseases incidence risk on different subgroups at specific ambient temperature.
Table S4 Cumulative relative risk of respiratory ED visits on different subgroups at specific ambient temperatures.Table S7 Cumulative relative risks of respiratory ED visits for specific temperatures with different lag days at a maximum lag of 10 days.

Fig. S2 .Fig. S3 .Fig. S4 .Fig. S5 .
Fig. S2.Flow chart of Meta analysis.4 Fig.S3.Time series distribution of daily respiratory ED visits and daily mean temperature in Beijing.5 Fig. S4.Scatterplot of daily respiratory ED visits and daily mean temperature in Beijing.6 Fig. S5.The graph of the sum of the absolute value of PACF and the df of time.7

Fig. S7 .
Fig. S7.Cumulative relative risk of respiratory ED visits on different subgroups at specific ambient temperatures.

Fig. S9 .
Fig. S9.Sensitivity analysis of the effect of daily mean air temperature on respiratory ED visit rates in Beijing (the lag effect after correcting for the interaction of different air pollution factors interacting with daily mean temperature).

Fig. S1 .
Fig. S1.Locations of major emergency stations and all monitoring stations

Fig. S3 .
Fig. S3.Time series distribution of daily respiratory ED visits and daily mean temperature in Beijing.

Fig. S4 .
Fig. S4.Scatterplot of daily respiratory ED visits and daily mean temperature in Beijing.

Fig. S5 .
Fig. S5.The graph of the sum of the absolute value of PACF and the df of time.

Fig S6 .
Fig S6.Relationship between temperature and risk of respiratory diseases development at different lag days in Beijing.

Fig. S7 .
Fig. S7.Cumulative relative risk of respiratory ED visits on different subgroups at specific ambient temperatures.

Fig. S8 .
Fig. S8.The sensitivity analysis of effect of daily mean temperature on respiratory ED visits with different related parameters in Beijing (Cumulative RR for lags 0-21 days at moderately cold temperatures (-4°C) for different lags or exposure dimensions).

Fig. S9 .
Fig. S9.Sensitivity analysis of the effect of daily mean air temperature on respiratory ED visit rates in Beijing (the lag effect after correcting for the interaction of different air pollution factors interacting with daily mean temperature).

Fig. S10
Fig. S10 Cumulative relative risks of respiratory ED visits for specific temperatures with different lag days at a maximum lag of 10 days.

Table S5
Basic information of literature in Meta-analysis of cold effect

Table S6
Basic information of literature in Meta-analysis of hot effect Fig.S8.The sensitivity analysis of effect of daily mean temperature on respiratory ED visits with different related parameters in Beijing (Cumulative RR for lags 0-21 days at moderately cold temperatures (-4°C) for different lags or exposure dimensions).

Table S2
Cumulative association and relative risk with different lag days of specific temperatures on respiratory ED visits.

Table S5
Basic information of literature in Meta-analysis of cold effect.

Table S7
Cumulative relative risks of respiratory ED visits for specific temperatures with different lag days at a maximum lag of 10 days.*p < 0.05 was considered statistically significant and is shown in bold type.